Field
Aspects of the present inventions relate to sales force automation or sales force management systems (hereinafter collectively referred to as “SFA”), Customer Relationship Management (hereinafter referred to as “CRM”), other systems that rely on data entry by users, data quality enforcement and facilitation for those systems, such as those associated with professional services organizations to manage productivity and ensure that billable activities are documented, as well as both big data analytics and business analytics systems. Among other things, innovations herein relate to systems and methods for providing user-centric tools that help companies to facilitate and enforce both user software adoption and data quality in user data entries; automate data collection tasks and distill large volumes of relevant data from telecom networks and devices, data networks and devices, billing systems, mobile and other computing and communications devices; shorten the time needed to make that data useful and available; provide real-time access to data while making that data more user-centric and consumable by business stakeholders; and translate large volumes of transactional data into business insight to drive business decision-making.
Aspects of the present inventions utilize a data-driven approach to solving the core data quality issues for systems that collect information from users via data entry, such as customer relationship management, sales force automation and professional services systems, and enhances those systems with actionable insight derived from objective data sources and the data quality facilitation and enforcement mechanisms described herein together with mechanisms for reducing the need for manual data entry by users of those systems and accelerating how users interact with those systems.
Description of Related Information
The burden of manual data entry for Customer Relationship Management, Sales Force Automation and professional services systems taxes both productivity and relationships between managers and front-line users, as users are continually asked to provide manually-entered situational updates into such systems in response to real-world events. The veracity of user data entries often complicates the challenges associated with accurate data collection and analysis of the data in the context of customer relationship management, sales force automation and professional services systems.
The volume, variety and velocity of newly available data sources in conjunction with the processes detailed herein provide an opportunity to alleviate the above-noted challenges and extract actionable insight from those data sources.
As such, some of the solutions/innovations herein are designed to make alleviate the burden of manual data entry, increase productivity, provide visibility and insight to both front-line users and managers and make sense of big data and other data sources in order to find patterns that help organizations better manage their data quality and employees, and gain new insights that enable them to make better financial projections and better manage processes related to sales and services.
Further, drawbacks of current sales force, customer relationship management and/or professional services systems often include or involve aspects of failing to effectively address one or more of 3 core issues, adoption, data integrity and/or productivity. For example, many such systems suffer with respect to adoption in being unable to address issues of people failing to enter relevant data into the system on a timely basis or at all.
Further, many suffer drawbacks with respect to data integrity, such as issues relating to sales pipeline projections that are often overly optimistic or recordation of time or other information related to services provided. Finally, such systems have drawbacks with respect to accurate productivity measures, such as when level(s) of productive activity may not bode well for future results in future quarters, but management may not have a reliability check on levels of sales or services productivity/processes that may be lacking.
In sum, there is a need for systems and methods that may adequately address these and other drawbacks.